The Statistical Performance Gap: High vs. Low Income Countries

Based on 27 low-income and 56 high-income countries (2023) • Pillars ordered by gap size (largest to smallest)

TidyTuesday
Data Visualization
R Programming
2025
Analyzing inequality in statistical capacity using World Bank’s Statistical Performance Indicators. High-income countries outperform low-income peers across all five pillars, with the largest gaps in data sources and infrastructure.
Published

November 23, 2025

Figure 1: Faceted line chart showing statistical performance gaps between high-income (teal) and low-income (purple) countries across five pillars from 2016 to 2023. Gray ribbons highlight the gaps between income groups. Data Sources shows the most significant gap at 43 points, followed by Data Infrastructure at 34 points and Data Services at 27 points. Data Use and Data Products show smaller gaps of 14 and 6 points, respectively. High-income countries consistently score higher across all pillars, with most gaps remaining stable or widening over time, except Data Products, which shows slight convergence.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,     # Easily Install and Load the 'Tidyverse'
    ggtext,        # Improved Text Rendering Support for 'ggplot2'
    showtext,      # Using Fonts More Easily in R Graphs
    janitor,       # Simple Tools for Examining and Cleaning Dirty Data
    scales,        # Scale Functions for Visualization
    ggrepel,       # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
    glue           # Interpreted String Literals
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 10,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 47)

spi_indicators <- tt$spi_indicators |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(spi_indicators)
skimr::skim(spi_indicators) |> summary()
```

4. Tidy Data

Show code
```{r}
#| label: tidy-fixed
#| warning: false

# clean base data
spi_tidy <- spi_indicators |>
  mutate(
    year = as.integer(year),
    income = factor(
      income,
      levels = c(
        "Low income", "Lower middle income",
        "Upper middle income", "High income"
      )
    )
  ) |>
  filter(!is.na(overall_score))

# reshape to long format for pillar analysis
pillar_long <- spi_tidy |>
  pivot_longer(
    cols = ends_with("_score") & !contains("overall"),
    names_to = "pillar",
    values_to = "score"
  ) |>
  mutate(
    pillar = str_remove(pillar, "_score") |>
      str_replace_all("_", " ") |>
      str_to_title()
  )

# Focus on High and Low income, calculate averages
pillar_gap_data <- pillar_long |>
  filter(income %in% c("Low income", "High income")) |>
  group_by(year, income, pillar) |>
  summarize(
    avg_score = mean(score, na.rm = TRUE),
    n_countries = n(),
    .groups = "drop"
  )

# Calculate gaps and create ordering
pillar_gaps <- pillar_gap_data |>
  select(year, income, pillar, avg_score) |>
  pivot_wider(names_from = income, values_from = avg_score) |>
  mutate(gap = `High income` - `Low income`)

# Get pillar order and apply to both datasets
pillar_order <- pillar_gaps |>
  filter(year == 2023) |>
  arrange(desc(gap)) |>
  pull(pillar)

# Apply ordering to both datasets
pillar_gap_data <- pillar_gap_data |>
  mutate(pillar = factor(pillar, levels = pillar_order))

ribbon_data <- pillar_gaps |>
  mutate(pillar = factor(pillar, levels = pillar_order))

# create facet labels with gap sizes
facet_labels_vec <- pillar_gaps |>
  filter(year == 2023) |>
  mutate(
    gap_rounded = round(gap, 0),
    facet_label = glue("{pillar}\n(Gap: {gap_rounded} pts)")
  ) |>
  select(pillar, facet_label) |>
  deframe()
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = list(
        low_income = "#9b59b6"  ,
        high_income = "#0f9a8a",
        gap_fill = "#c9d0d8"
    )
)

### |- titles and caption ----
title_text <- "The Statistical Performance Gap: High vs. Low Income Countries"

subtitle_text <- str_glue(
    "Based on 27 low-income and 56 high-income countries (2023) • Pillars ordered by gap size (largest to smallest)<br>",
    "High-income countries consistently outperform low-income peers across all pillars.<br><br>",
    "Think of this as a **health checkup for a country's data ecosystem:**<br><br>",
    "**Data Sources** = Census, surveys, admin data quality • **Data Infrastructure** = Laws, IT systems, trained staff<br>",
    "**Data Services** = Open portals, timely releases • **Data Use** = Are stats used by policymakers? • **Data Products** = GDP, health, SDG indicators"
)

caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 46,
    source_text = "World Bank Statistical Performance Indicators"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_markdown(
      face = "bold", family = fonts$title, size = rel(1.4),
      color = colors$title, margin = margin(b = 10), hjust = 0
    ),
    plot.subtitle = element_text(
      face = "italic", family = fonts$subtitle, lineheight = 1.2,
      color = colors$subtitle, size = rel(0.9), margin = margin(b = 20), hjust = 0
    ),

    # Grid
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(color = "gray90", linewidth = 0.3),

    # Axes
    axis.title = element_text(size = rel(0.8), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.85)),
    axis.ticks = element_blank(),

    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
      face = "bold",
      color = "gray20",
      size = rel(0.9),
      margin = margin(t = 6, b = 4)
    ),
    panel.spacing = unit(1.5, "lines"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.8), face = "bold"
    ),
    legend.text = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.7)
    ),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

### |-  main plot ----
p <- 
  ggplot() +
  # Geoms
  geom_ribbon(
    data = ribbon_data,
    aes(x = year, ymin = `Low income`, ymax = `High income`),
    fill = colors$palette$gap_fill,
    alpha = 0.6
  ) +
  geom_line(
    data = pillar_gap_data,
    aes(x = year, y = avg_score, color = income, group = income),
    linewidth = 1.2,
    alpha = 0.9
  ) +
  geom_point(
    data = pillar_gap_data,
    aes(x = year, y = avg_score, color = income),
    size = 2.5
  ) +
  # Facets
  facet_wrap(
    ~pillar,
    ncol = 3,
    labeller = labeller(pillar = facet_labels_vec)
  ) +
  # Scales
  scale_x_continuous(breaks = c(2016, 2020, 2023)) +
  scale_y_continuous(
    limits = c(0, 100),
    breaks = seq(0, 100, 25),
    labels = label_number()
  ) +
  scale_color_manual(
    values = c(
      "Low income" = colors$palette$low_income,
      "High income" = colors$palette$high_income
    )
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "Average SPI Score",
    # color = NULL,
  ) +
  # Theme
  theme(
    plot.title = element_markdown(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.15,
      margin = margin(t = 8, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.7),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.88),
      lineheight = 1.2,
      margin = margin(t = 4, b = 0)
    ),
    plot.caption = element_markdown(
      size = rel(0.56),
      family = "Arial",
      color = colors$caption,
      hjust = 0,
      lineheight = 1.4,
      margin = margin(t = 15, b = 5)
    ),
    # Legend
    legend.position = "top",
    legend.justification = "left",
    legend.text = element_text(size = 11),
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 47, 
  width  = 10,
  height = 10,
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      glue_1.8.0      ggrepel_0.9.6   scales_1.3.0   
 [5] janitor_2.2.0   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          httr2_1.0.6        htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.3     parallel_4.4.0     curl_6.0.0         gifski_1.32.0-1   
[13] fansi_1.0.6        pkgconfig_2.0.3    skimr_2.1.5        lifecycle_1.0.4   
[17] farver_2.1.2       compiler_4.4.0     textshaping_0.4.0  munsell_0.5.1     
[21] repr_1.1.7         codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0   
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0     
[37] grid_4.4.0         colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3    
[41] base64enc_0.1-3    utf8_1.2.4         withr_3.0.2        rappdirs_0.3.3    
[45] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29     tidytuesdayR_1.1.2
[49] gitcreds_0.1.2     bit_4.5.0          ragg_1.3.3         hms_1.1.3         
[53] evaluate_1.0.1     knitr_1.49         markdown_1.13      rlang_1.1.6       
[57] gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3        
[61] vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9    
[65] R6_2.5.1           systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_47.qmd.

For the full repository, click here.

10. References

  1. Data Source:

11. Custom Functions Documentation

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

  • fonts.R: setup_fonts(), get_font_families() - Font management with showtext
  • social_icons.R: create_social_caption() - Generates formatted social media captions
  • image_utils.R: save_plot() - Consistent plot saving with naming conventions
  • base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils

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